title: Fully automated segmentation of corpus callosum in midsagittal brain MRIs.
abstract: In the diagnosis of various brain disorders by analyzing the brain magnetic resonance images (MRI), the segmentation of corpus callosum (CC) is a crucial step. In this paper, we propose a fully automated technique for CC segmentation in the T1-weighted midsagittal brain MRIs. An adaptive mean shift clustering technique is first used to cluster homogenous regions in the image. In order to distinguish the CC from other brain tissues, area analysis, template matching, in conjunction with the shape and location analysis are proposed to identify the CC area. The boundary of detected CC area is then used as the initial contour in the Geometric Active Contour (GAC) model, and evolved to get the final segmentation result. Experimental results demonstrate that the proposed technique overcomes the problem of manual initialization in existing GAC technique, and provides a reliable segmentation performance.
authors.forename: Yue
authors.initials: Y
authors.lastname: Li
authors.forename: Mrinal
authors.initials: M
authors.lastname: Mandal
authors.forename: S Nizam
authors.initials: SN
authors.lastname: Ahmed
mesh_terms: D000465:Algorithms; D001331:Automation; D003337:Corpus Callosum / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*